Identification algorithm for systemic arterial parameters with application to total artificial heart control.

A new algorithm for estimating systemic arterial parameters from systolic pressure and flow measurements at the root of the aorta is developed and tested through a systems identification approach. The resulting procedure has direct application to a total artificial heart (TAH) control system currently under development. Identification models, representing the systemic arterial system, are developed from existing work in the area of cardiovascular modeling. The resistive and compliance components of these models are physically significant, representing overall hydraulic properties of the systemic arterial system. A unique method of parameterizing the identification models is designed which operates on the basis of aortic pressure and flow measurements taken exclusively during systole. The estimator is a modified recursive least squares algorithm which utilizes covariance modification to track time-varying parameters and a dead-zone to improve the robustness. Performance of the estimation algorithm was tested on data generated by a higher-order distributed model of the systemic arterial bed using normal canine parameters. Results from model-to-model experiments verify the consistency of the estimates and the ability of the estimator to converge quickly and track dynamically varying parameters.